




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase=5   ):
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  assert masked_input.count("""<mask>"""   ) == 1
  UpperCAmelCase__         = torch.tensor(tokenizer.encode(UpperCamelCase__ ,  add_special_tokens=UpperCamelCase__   )   ).unsqueeze(0   )  # Batch size 1
  UpperCAmelCase__         = model(UpperCamelCase__   )[0]  # The last hidden-state is the first element of the output tuple
  UpperCAmelCase__         = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  UpperCAmelCase__         = logits[0, masked_index, :]
  UpperCAmelCase__         = logits.softmax(dim=0   )
  UpperCAmelCase__    ,       UpperCAmelCase__         = prob.topk(k=UpperCamelCase__ ,  dim=0   )
  UpperCAmelCase__         = """ """.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(UpperCamelCase__   )   )]   )
  UpperCAmelCase__         = tokenizer.mask_token
  UpperCAmelCase__         = []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """   )   ):
    UpperCAmelCase__         = predicted_token_bpe.replace("""\u2581""" ,  """ """   )
    if " {0}".format(UpperCamelCase__   ) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(""" {0}""".format(UpperCamelCase__   ) ,  UpperCamelCase__   ),
              values[index].item(),
              predicted_token,
          )   )
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(UpperCamelCase__ ,  UpperCamelCase__   ),
              values[index].item(),
              predicted_token,
          )   )
  return topk_filled_outputs


snake_case__			:    List[Any]			     =      CamembertTokenizer.from_pretrained('''camembert-base''')
snake_case__			:    Optional[Any]			     =      CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

snake_case__			:    Optional[int]			     =      '''Le camembert est <mask> :)'''





print(fill_mask(masked_input, model, tokenizer, topk=3))





'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
